Abstract #4861

Fetal Pose Estimation via Deep Neural Network by Detection of Fetal Joints, Eyes, and Bladder

Molin Zhang¹, Junshen Xu², Esra Turk³, Larry Zhang^2,4, P.Ellen Grant^3,4, Karen Ying¹, Polina Golland^2,4, and Elfar Adalsteinsson^2,5

¹Department of Engineering Physics, Tsinghua university, BeiJing, China, ²Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, ³Harvard Medical School, Boston, MA, United States, ⁴Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, ⁵Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States

Neural networks and deep learning have achieved great success in human pose estimation through the identification of key human points in conventional photography and video. We propose fetal pose estimation in a time series of echo planar MRI volumes of the pregnant abdomen via deep learning algorithms for detection of key fetal landmarks, including joints, eyes, and bladder. Fetal pose estimation in an EPI time series yields novel means of quantifying fetal movements in health and disease, and enables learning of kinematic models that may enhance mitigation of fetal motion artifacts during MRI acquisition.

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